Chemometrics/informatics, and data analysis in general, are increasingly important in x-ray photoelectron spectroscopy (XPS) because of the large amount of information (spectra/data) that is often collected in degradation, depth profiling, operando, and imaging studies. In this guide, we present chemometrics/informatics analyses of XPS data using a summary statistic (pattern recognition entropy), principal component analysis, multivariate curve resolution (MCR), and cluster analysis. These analyses were performed on C 1s, O 1s, and concatenated (combined) C 1s and O 1s narrow scans obtained by repeatedly analyzing samples of cellulose and tartaric acid, which led to their degradation. We discuss the following steps, principles, and methods in these analyses: gathering/using all of the information about samples, performing an initial evaluation of the raw data, including plotting it, knowing which chemometrics/informatics analyses to choose, data preprocessing, knowing where to start the chemometrics/informatics analysis, including the initial identification of outliers and unexpected features in data sets, returning to the original data after an informatics analysis to confirm findings, determining the number of abstract factors to keep in a model, MCR, including peak fitting MCR factors, more complicated MCR factors, and the presence of intermediates revealed through MCR, and cluster analysis. Some of the findings of this work are as follows. The various chemometrics/informatics methods showed a break/abrupt change in the cellulose data set (and in some cases an outlier). For the first time, MCR components were peak fit. Peak fitting of MCR components revealed the presence of intermediates in the decomposition of tartaric acid. Cluster analysis grouped the data in the order in which they were collected, leading to a series of average spectra that represent the changes in the spectra. This paper is a companion to a guide that focuses on the more theoretical aspects of the themes touched on here.

Peak fitting is frequently performed in X-ray photoelectron spectroscopy (XPS). However, recent reports suggest that the current quality of this peak fitting is often inadequate in the scientific literature. Various statistical methods may be advantageously applied to an XPS peak fit to help determine the quality and validity of a fit. In this paper we describe a new statistical tool, which we believe will be helpful for determining the quality of protocols for fitting XPS data. This tool, box plots of random starting conditions, helps identify multiple local minima in a fit space. That is, ideally, different, reasonable starting conditions for a fit should lead to the same result, i.e., ideally, there should be a single global minimum for a fitting protocol. To determine whether a fit space contains multiple local minima, a series of reasonable, random starting conditions are chosen for the fit. If the boxes in the box plot of the peak areas of these fits are narrow, the different possibilities converge to a single global minimum. Conversely, if the boxes are wide, multiple local minima are present. Our approach is similar to the mathematical concept of 'disproof by contradiction'. It is demonstrated herein in four-and tencomponent fits to a moderately complex C 1s narrow scan. The resulting box plots compare favorably to traditional Monte Carlo analyses and uniqueness plots, although each of these statistical tools performs a different function/probes the fit space differently.